The uncertainty of drought forecasting based on past meteorological data is increasing because of climate change. However, agricultural droughts, associated with food resources and determined by soil moisture, must be predicted several months ahead for timely resource allocation. Accordingly, we designed a severe drought area prediction (SDAP) model for short-term drought without meteorological data. The predictions of our proposed SDAP model indicate a forecast of serious drought areas assuming non-rainfall, not a probability prediction of drought occurrence. Furthermore, this prediction provides more practical information to help with rapid water allocation during a real drought. The model structure using remote sensing data consists of two parts. First, the drought function f(x) from the training area by random forest (RF) learned the changes in the pattern of soil moisture index (SMI) from the past drought and the training performance was found to be root mean square error (RMSE) = 0.052, mean absolute error (MAE) = 0.039, R 2 = 0.91. Second, derived f(x) predicted the SMI of the study area, which is 20 times larger than the training area, of the same season of another year as RMSE = 0.382, MAE = 0.375, R 2 = 0.58. We also obtained the variable importance stemming from RF and discussed its meaning along with the advantages and limitations of the model, training areas selection, and prediction coverage.Water 2019, 11, 705 2 of 15 resources. Droughts are generally classified into four categories, namely, meteorological, hydrological, agricultural, and socio-economic droughts [9].In recent years, the prevalence of machine learning methodologies, and frequent droughts and floods around the world, have increased the prediction of agricultural drought. However, the uncertainties of prediction caused by combination of meteorological factors [10][11][12][13], still remain a problem. According to the Intergovernmental Panel on Climate Change (IPCC) AR5 guidance note, the complex use of different models, complexity of models, and inclusion of additional processes in the analysis are the main reasons for the increase in uncertainties [14]. Thus, the complex models used for the integrated analysis of meteorological and agricultural drought have increased the uncertainty in the results [14]. However, agricultural drought prediction methods that do not include spatial information, and are only based on precipitation, such those put forth in [12,15], cannot predict the spatial distribution of agricultural drought. Another difficulty in predicting agricultural drought stems from predictions based on the meteorological pattern, such as patterns of past precipitation. Even well-designed agricultural drought models, based on meteorological factors, cannot make accurate predictions because of the shifts in existing patterns, due to climate change. According to a study of agricultural forecasting models from 2007 to 2017, (see Table 1 in [16]), precipitation was used as an input variable in almost all the models.Previous studies ...